Scalable semantic web data management using vertical partitioning
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
RDF-3X: a RISC-style engine for RDF
Proceedings of the VLDB Endowment
Matrix "Bit" loaded: a scalable lightweight join query processor for RDF data
Proceedings of the 19th international conference on World wide web
Concise: Compressed 'n' Composable Integer Set
Information Processing Letters
gStore: answering SPARQL queries via subgraph matching
Proceedings of the VLDB Endowment
QueryPIE: backward reasoning for OWL horst over very large knowledge bases
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
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When querying RDF data, one may use reasoning to reach intensional data, i.e., data defined by sets of rules. This is usually achieved through forward chaining, with space and maintenance overheads, or backward chaining, with high query evaluation and optimization costs. Recent approaches rely on pre-computing the terminological closure of the data rather than the full saturation. In this setting, one can even query the data without resorting to backward chaining, using a so-called semantic index. However, these techniques are limited in the type of queries they can support. In this paper, we introduce a data storage technique which mitigates the space issues of forward-chaining. We show that it can also be used with a semantic index. We propose a new structure for the index that relies on bitmaps making it resilient to updates. Our experimental results demonstrate that our storage model significantly reduces the space required to store the data. We show that the indexes can be computed quickly and fit well in memory even for very large ontologies. Finally, we analyze how query answering is affected by the data layout.